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Bayesian Testing Of Granger Causality In Functional Time Series

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  • Rituparna Sen
  • Anandamayee Majumdar
  • Shubhangi Sikaria

Abstract

We develop a multivariate functional autoregressive model (MFAR), which captures the cross-correlation among multiple functional time series and thus improves forecast accuracy. We estimate the parameters under the Bayesian dynamic linear models (DLM) framework. In order to capture Granger causality from one FAR series to another we employ Bayes Factor. Motivated by the broad application of functional data in finance, we investigate the causality between the yield curves of two countries. Furthermore, we illustrate a climatology example, examining whether the weather conditions Granger cause pollutant daily levels in a city.

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  • Rituparna Sen & Anandamayee Majumdar & Shubhangi Sikaria, 2021. "Bayesian Testing Of Granger Causality In Functional Time Series," Papers 2112.15315, arXiv.org.
    • Handle: RePEc:arx:papers:2112.15315
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